Detection System and Method

ABSTRACT

A detection system includes a radar-unit and a controller-circuit. The radar-unit is configured to detect objects proximate a host-vehicle. The controller-circuit is in communication with the radar-unit and is configured to determine a detection-distribution based on the radar-unit. The detection-distribution is characterized by a longitudinal-distribution of zero-range-rate detections associated with a trailer towed by the host-vehicle. The controller-circuit is further configured to determine a trailer-classification based on a comparison of the detection-distribution and longitudinal-distribution-models stored in the controller-circuit. The trailer-classification is indicative of a dimension of the trailer. The controller-circuit determines a trailer-length of the trailer based on the detection-distribution and the trailer-classification.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 16/154,848, filed Oct. 9, 2018, which in turn claims priority toU.S. Provisional Application Ser. No. 62/742,646, filed Oct. 8, 2018,the disclosures of which are incorporated herein by reference.

TECHNICAL FIELD OF INVENTION

This disclosure generally relates to a detection system, and moreparticularly relates to a detection system that determines atrailer-length.

BRIEF DESCRIPTION OF DRAWINGS

The present invention will now be described, by way of example withreference to the accompanying drawings, in which:

FIG. 1 is an illustration of a detection system in accordance with oneembodiment;

FIG. 2 is an illustration of the detection system of FIG. 1 inaccordance with one embodiment;

FIG. 3A is a plot of objects detected by the detection system of FIG. 1in accordance with one embodiment;

FIG. 3B is a plot of the objects of FIG. 3A in a longitudinal directionin accordance with one embodiment;

FIG. 4A is a plot of objects detected by the detection system of FIG. 1in accordance with one embodiment;

FIG. 4B is a plot of the objects of FIG. 4A in a longitudinal directionin accordance with one embodiment;

FIG. 5A is a plot of objects detected by the detection system of FIG. 1in accordance with one embodiment;

FIG. 5B is a plot of the objects of FIG. 5A in a longitudinal directionin accordance with one embodiment;

FIG. 6 is an illustration of an iterative process determining alongitudinal-distribution-model in accordance with one embodiment; and

FIG. 7 is an illustration of a detection method in accordance withanother embodiment.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

FIG. 1 illustrates a non-limiting example of a detection system 10,hereafter referred to as the system 10, installed on a host-vehicle 12towing a trailer 14. As will be described in more detail below, thesystem 10 in an improvement over other detection systems because thesystem 10 estimates a trailer-length 16 based on detected targets byclassifying a distribution of data points and performing a regression onthe distribution of the data points. The system 10 provides thetechnical benefit of enabling an adjustment of a blind-zone (not shown)of the host-vehicle 12 based on a size of the trailer 14, improvingsafety for the driver and other vehicles. In one embodiment, the trailer14 is a cargo-trailer that may be an enclosed-type with solid panels,while in another embodiment the cargo-trailer is an open-type with anexposed frame. In yet another embodiment the trailer 14 is aboat-trailer. In yet another embodiment the trailer 14 is atravel-trailer.

The system 10 includes a radar-unit 20. The radar-unit 20 is configuredto detect objects 26 proximate the host-vehicle 12. The radar-unit 20detects a radar-signal that is reflected by the features of the trailer14 towed by the host-vehicle 12, as illustrated in FIG. 2. Typicalradar-systems on vehicles are capable of only determining a distance 28(i.e. range) and azimuth-angle 30 to the target so may be referred to asa two-dimensional (2D) radar-system. Other radar-systems are capable ofdetermining an elevation-angle to the target so may be referred to as athree-dimensional (3D) radar-system. In the non-limiting exampleillustrated in FIG. 1, the 2D radar-unit 20 includes a left-sensor 20Aand a right-sensor 20B. A radar sensor-system with a similarlyconfigured radar-unit 20 is available from Aptiv of Troy, Mich., USA andmarketed as an Electronically Scanning Radar (ESR) or aRear-Side-Detection-System (RSDS). It is contemplated that the teachingspresented herein are applicable to radar-systems with one or more sensordevices. It is also contemplated that the teachings presented herein areapplicable to both 2D radar-systems and 3-D radar-systems with one ormore sensor devices, i.e. multiple instances of the radar-unit 20. Theradar-unit 20 is generally configured to detect the radar-signal thatmay include data indicative of the detected-target present on thetrailer 14. As used herein, the detected-target present on the trailer14 may be a feature of the trailer 14 that is detected by the radar-unit20 and tracked by a controller-circuit 32, as will be described in moredetail below.

Referring back to FIG. 1, the system 10 also includes thecontroller-circuit 32 in communication with the radar-unit 20. Theradar-unit 20 may be hardwired to the controller-circuit 32 through thehost-vehicle's 12 electrical-system (not shown), or may communicatethrough a wireless network (not shown). The controller-circuit 32 mayinclude a processor (not shown) such as a microprocessor or othercontrol circuitry such as analog and/or digital control circuitryincluding an application specific integrated circuit (ASIC) forprocessing data as should be evident to those in the art. Thecontroller-circuit 32 includes a memory 22, includingnon-volatile-memory, such as electrically-erasable-programmableread-only-memory (EEPROM) for storing one or more routines, thresholds,and captured data. The one or more routines may be executed by theprocessor to perform steps for detecting the objects 26 based on signalsreceived by the controller-circuit 32 from the radar-unit 20 asdescribed herein. The controller-circuit 32 is configured to determinethat the trailer 14 is being towed by the host-vehicle 12 (i.e.determine a trailer-presence) using the known methods of zero-range-rate(ZRR) detection of targets that will be understood by those in the art.

FIG. 3A illustrates a plot of multiple radar-sensors 20A, 20B dataacquisition cycles that locate the ZRR targets along ahost-vehicle-longitudinal-axis 34 and a host-vehicle-lateral-axis 36.The trailer 14 has a known-trailer-length of 3.2 m. Each dataacquisition cycle consists of 64-detections per radar-sensor 20A, 20Bwithin a time interval of 50-milliseconds (50 ms), or a total of128-detections for the two radar-sensors 20A and 20B. The origin of theplot is located at a center of the host-vehicle's 12 front-bumper (notspecifically shown).

FIG. 3B illustrates a detection-distribution 24 determined by thecontroller-circuit 32 that is characterized by alongitudinal-distribution of ZRR detections associated with the trailer14 towed by the host-vehicle 12. That is, the detection-distribution 24is a plot of the groups of the ZRR targets from FIG. 3A along thehost-vehicle-longitudinal-axis 34 only. Note that the x-axis for theplot in FIG. 3B is the distance 28 from a rear-end of the host-vehicle12, and not the distance from the front-bumper as illustrated in FIG.3A. The controller-circuit 32 determines the detection-distribution 24in a finite time-period, which in the examples illustrated herein, isabout 1-minute in duration.

The detection-distribution 24 is characterized by groups of ZRR targetsdetected within sequential predetermined length-intervals extending fora predetermined-distance 38 behind the host-vehicle 12. In the examplesillustrated herein, the groups represent the ZRR targets detected inincrements of 0.2-meters (0.2 m) extending from the rear-end of thehost-vehicle 12 for the distance 28 of up to about 12 m. For example,every 10 points along the x-axis of the plot in FIG. 3B represents 2.0 mof distance 28 from the rear-end of the 5 m long host-vehicle 12. TheY-axis in FIG. 3B represents the cumulative number of detections in agroup. Some of the groups represent real-objects and others representphantom-objects. Experimentation by the inventors has discovered thatthe predetermined length-intervals of less than or equal to about0.2-meters provides an adequate balance between memory 22 utilizationand accuracy of the trailer-length 16 determination. Thepredetermined-distance 38 of 12 m is selected as representative of atypical longest-trailer that may be legally towed on roadways by thehost-vehicle 12. However, the predetermined-distance 38 may be userdefined and adjusted to other distances 28 in excess of 12 m.

Referring again to FIG. 1, the controller-circuit 32 is furtherconfigured to determine a trailer-classification 42 based on acomparison of the detection-distribution 24 andlongitudinal-distribution-models 44 stored in the controller-circuit 32.The trailer-classification 42 is indicative of a dimension of thetrailer 14 and includes a first-class 46 (e.g. trailers 14 having atrailer-length 16 between 1 m and 4 m), a second-class 48 (e.g. trailers14 having the trailer-length 16 between 4 m and 8 m), and a third-class50 (e.g. trailers 14 having the trailer-length 16 between 8 m and 12 m).The longitudinal-distribution-models 44 are trained (i.e. calibrated oroptimized) to determine the trailer-classification 42 using known data(i.e. training-data collected from the detection-distributions 24 ofvarious trailers 14 with known-trailer-lengths) using a machine learningalgorithm with Supervised Learning (e.g., “examples” x with “labels” y),wherein the x-training-data are the cumulative-detections at each of thepredetermined length-intervals (i.e., every 0.2 m up to 12 m), and they-training-data are the associated known-trailer-classification (i.e.,first-class 46, second-class 48, and third-class 50). The machinelearning algorithm creates a model based on the training-data thatdetermines the trailer-classification 42. Any applicable machinelearning algorithm may be used to develop thelongitudinal-distribution-models 44. One such machine learning algorithmis the MATLAB® “fitensemble( )” by The MathWorks, Inc. of Natick, Mass.,USA. The prediction of the trailer-classification 42 based on thelongitudinal-distribution-models 44 and the detection-distribution 24 isexecuted using the MATLAB® “predict( )” function, by The MathWorks, Inc.of Natick, Mass., USA, or similarly known algorithm In the exampleillustrated in FIG. 3A, the trailer 14 is classified by the system 10 asa first-class 46 trailer 14.

The controller-circuit 32 determines the trailer-length 16 based on thedetection-distribution 24 and the trailer-classification 42 by applyingregression-models 52 to the detection-distribution 24. Theregression-models 52 are associated with each of thetrailer-classifications 42 and are stored in the controller-circuit 32.Each trailer-classification 42 has associated with it a uniqueregression-model 52 to more accurately determine the trailer-length 16.The regression-models 52 are trained to determine the trailer-length 16using known training-data using the same machine learning algorithm withsupervised learning as described above, wherein the x-training-data arethe cumulative-detections at each of the predetermined length-intervals(i.e., every 0.2 m) and the y-training-data are the associatedknown-trailer-lengths. The regression-models 52 are developed using theMATLAB® “fitrensemble( )” by The MathWorks, Inc. of Natick, Mass., USA,and use 50 iterations to converge on the model having an acceptableerror or residual values. The controller-circuit 32 uses thedetection-distribution 24 as input into the regression-model 52 toestimate or predict the trailer-length 16. The prediction of thetrailer-length 16 is also executed using the MATLAB® “predict( )”function, by The MathWorks, Inc. of Natick, Mass., USA, or similarlyknown algorithm, based on the regression-model 52 and thedetection-distribution 24.

In the example illustrated in FIG. 3B the trailer-length 16 is predictedto be 3.22 m compared to the known length of 3.20 m. FIGS. 4A-4Billustrate the trailer 14 classified as the second-class 48 with theknown length of 6.60 m, and the trailer-length 16 predicted by thesystem 10 of 6.58 m. FIGS. 5A-5B illustrate the trailer 14 classified asthe third-class 50 with the known length of 8.90 m, and trailer-length16 predicted by the system 10 of 8.92 m. Experimentation by theinventors has discovered that the prediction of the trailer-length 16using the above system 10 has been shown to reduce the error to lessthan 1.5% of the known-trailer-length.

FIG. 6 illustrates an example of an iterative process for determiningthe longitudinal-distribution-models 44 using the MATLAB® functionsdescribed above and known training-data. Iteration-1 initially applies alinear function representing a mean value of the training-data, afterwhich an error residual (i.e. a difference between the mean-value andthe particular data-point) is calculated. The plot of the error residualfrom Iteration-1 is fit with a step-function which is used to update thelinear function in iteration-2. The iterative process continues for Niterations (preferably N=50) until the resultinglongitudinal-distribution-model 44 is characterized as having the errorresidual close to zero.

FIG. 7 is a flow chart illustrating another embodiment of a detectionmethod 100.

Step 102, DETECT OBJECTS, includes detecting objects 26 proximate ahost-vehicle 12 with a radar-unit 20 as described above.

Step 104, DETERMINE DETECTION-DISTRIBUTION, includes determining thedetection-distribution 24 based on the radar-unit 20 with thecontroller-circuit 32 in communication with the radar-unit 20. Thedetection-distribution 24 is characterized by alongitudinal-distribution of zero-range-rate detections associated witha trailer 14 towed by the host-vehicle 12. The detection-distribution 24is determined in a finite time-period of about 1-minute. Thecontroller-circuit 32 detects the groups of zero-range-rate detectionswithin the sequential predetermined length-intervals extending for apredetermined-distance 38 behind the host-vehicle 12 as described above.

Step 106, DETERMINE TRAILER-CLASSIFICATION, includes determining thetrailer-classification 42, with the controller-circuit 32, based on acomparison of the detection-distribution 24 and thelongitudinal-distribution-models 44 stored in the controller-circuit 32.The trailer-classifications 42 include a first-class 46, a second-class48, and a third-class 50 as described above.

Step 108, DETERMINE TRAILER-LENGTH, includes determining thetrailer-length 16 of the trailer 14, with the controller-circuit 32,based on the detection-distribution 24 and the trailer-classification 42as described above. The trailer-length 16 is determined byregression-models 52 stored in the memory 22 of the controller-circuit32 as described above. Each trailer-classification 42 has a uniqueregression-model 52.

Accordingly, a detection system 10 (the system 10), a controller-circuit32 for the system 10, and a detection method 100 are provided. Thesystem 10 is an improvement over other detection systems because thesystem 10 estimates the trailer-length 16 in a time-period of less than1-minute and reduces a measurement error.

While this invention has been described in terms of the preferredembodiments thereof, it is not intended to be so limited, but ratheronly to the extent set forth in the claims that follow. “One or more”includes a function being performed by one element, a function beingperformed by more than one element, e.g., in a distributed fashion,several functions being performed by one element, several functionsbeing performed by several elements, or any combination of the above. Itwill also be understood that, although the terms first, second, etc.are, in some instances, used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first contactcould be termed a second contact, and, similarly, a second contact couldbe termed a first contact, without departing from the scope of thevarious described embodiments. The first contact and the second contactare both contacts, but they are not the same contact. The terminologyused in the description of the various described embodiments herein isfor the purpose of describing particular embodiments only and is notintended to be limiting. As used in the description of the variousdescribed embodiments and the appended claims, the singular forms “a”,“an” and “the” are intended to include the plural forms as well, unlessthe context clearly indicates otherwise. It will also be understood thatthe term “and/or” as used herein refers to and encompasses any and allpossible combinations of one or more of the associated listed items. Itwill be further understood that the terms “includes,” “including,”“comprises,” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. As used herein, the term“if” is, optionally, construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” is, optionally, construed to mean “upondetermining” or “in response to determining” or “upon detecting [thestated condition or event]” or “in response to detecting [the statedcondition or event],” depending on the context.

What is claimed is:
 1. A system comprising: a controller circuitconfigured to: determine, with a radar unit of a host vehicle, adistribution of zero-range-rate detections obtained from behind the hostvehicle; determine, based on the distribution of zero-range-ratedetections, a length of a trailer being towed behind the host vehicle;and control the host vehicle based on the length of the trailer.
 2. Thesystem of claim 1, wherein the controller circuit is configured todetermine the distribution of zero-range-rate detections during a finitetime period.
 3. The system of claim 1, wherein the controller circuit isconfigured to determine the length of the trailer by: inputting thedistribution of zero-range-rate detections into a model configured topredict a trailer classification; and determining, based on the trailerclassification, the length of the trailer.
 4. The system of claim 3,wherein the model is configured to predict the trailer classification tobe one of a plurality of different classifications.
 5. The system ofclaim 4, wherein the plurality of different classifications comprise: afirst class of trailers that are less than a first length, a secondclass of trailers that are between the first length and a second length,and a third class of trailers that are greater than the second length.6. The system of claim 4, wherein the model comprises one or moreregression models.
 7. The system of claim 6, wherein each of the one ormore regression models is associated with a different classificationfrom the plurality of different classifications.
 8. The system of claim1, wherein the distribution of zero-range-rate detections includesgroups of zero-range-rate targets detected at sequential intervals ofpotential trailer lengths that extend behind the host vehicle.
 9. Thesystem in accordance with claim 8, wherein the intervals of potentialtrailer lengths are each less than or equal to about 0.2-meters.
 10. Asystem comprising: a controller circuit configured to: determine adistribution of radar detections obtained from behind a host vehicle;input the distribution of radar detections to a regression modelconfigured to classify a trailer being towed behind the host vehicle;and control the host vehicle based on a trailer classification predictedby the regression model based on the distribution of radar detectionsobtained from behind the host vehicle.
 11. The system of claim 10,wherein the regression model comprises one or more regression modelsconfigured to predict the trailer classification to be one of aplurality of different classifications including the trailerclassification predicted by the regression model, and wherein each ofthe one or more regression models is associated with a differentclassification from the plurality of different classifications.
 12. Thesystem of claim 11, wherein the plurality of different classificationscomprise: a first class of trailers that are less than a first length, asecond class of trailers that are between the first length and a secondlength, and a third class of trailers that are greater than the secondlength.
 13. The system of claim 10, wherein the distribution of radardetections includes groups of zero-range-rate targets detected atsequential intervals of potential trailer lengths that extend behind thehost vehicle.
 14. The system of claim 13, wherein the sequentialintervals of potential trailer lengths are each less than or equal toabout 0.2-meters.
 15. The system of claim 13, wherein the controllercircuit is configured to determine the groups of zero-range-rate targetsduring a finite time period.
 16. The system of claim 10, wherein thecontroller circuit is further configured to: determine a length of thetrailer based on the trailer classification predicted by the regressionmodel; and control the host vehicle based on the length of the trailer.17. A method comprising: determining, by a controller circuit of a hostvehicle, a distribution of radar detections obtained from behind a hostvehicle; inputting, by the controller circuit, the distribution of radardetections to a regression model configured to classify a trailer beingtowed behind the host vehicle; and controlling the host vehicle based ona trailer classification predicted by the regression model based on thedistribution of radar detections obtained from behind the host vehicle.18. The method of claim 17, wherein the regression model comprises oneor more regression models configured to predict the trailerclassification to be one of a plurality of different classificationsincluding the trailer classification predicted by the regression model,and wherein each of the one or more regression models is associated witha different classification from the plurality of differentclassifications.
 19. The method of claim 18, wherein the plurality ofdifferent classifications comprise: a first class of trailers that areless than a first length, a second class of trailers that are betweenthe first length and a second length, and a third class of trailers thatare greater than the second length.
 20. The method of claim 17, furthercomprising: determining a distribution of zero-range-rate detectionsobtained from behind the host vehicle; determining, based on thedistribution of zero-range-rate detections, a length of the trailerbeing towed behind the host vehicle; and controlling the host vehiclebased further on the length of the trailer.